The role of quality of life data as an endpoint for collecting real-world evidence within geroscience clinical trials

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Highlights

• Collecting real-world evidence (RWE) may accelerate validation of gero-interventions.
• Quality of life (QoL) assays evaluate aspects of functional health and wellness.
• QoL is integral to healthy aging but not currently captured by aging biomarkers.
• Using QoL data as an endpoint can accelerate RWE data collection for geroscience.
• We propose the SF-36 as a well-suited QoL endpoint for geroscience clinical trials.

Abstract

With geroscience research evolving at a fast pace, the need arises for human randomized controlled trials to assess the efficacy of geroprotective interventions to prevent age-related adverse outcomes, disease, and mortality in normative aging cohorts. However, to confirm efficacy requires a long-term and costly approach as time to the event of morbidity and mortality can be decades. While this could be circumvented using sensitive biomarkers of aging, current molecular, physiological, and digital endpoints require further validation. In this review, we discuss how collecting real-world evidence (RWE) by obtaining health data that is amenable for collection from large heterogeneous populations in a real-world setting can help speed up validation of geroprotective interventions. Further, we propose inclusion of quality of life (QoL) data as a biomarker of aging and candidate endpoint for geroscience clinical trials to aid in distinguishing healthy from unhealthy aging. We highlight how QoL assays can aid in accelerating data collection in studies gathering RWE on the geroprotective effects of repurposed drugs to support utilization within healthy longevity medicine. Finally, we summarize key metrics to consider when implementing QoL assays in studies, and present the short-form 36 (SF-36) as the most well-suited candidate endpoint.

Keywords

Longevity

Geroscience

Quality of life

Real world evidence

Healthy aging

1. Introduction

Chronological age is the main non-modifiable risk factor for chronic disease and frailty (Niccoli and Partridge, 2012). The World Health Organization (WHO) reports that most people can expect to live up to 60 years in good health. However, after reaching age 60, it is estimated that they will live an average of 22 years more. To avoid an ever-expanding population of older adults with multiple chronic and debilitating conditions, there is a need to modify the aging process to promote healthy aging and optimize health at all ages (Wang et al., 2023). Fortunately, geroscience research is revealing that the process of biological aging, a reflection of damage accumulation and cellular dysfunction in the body, can be modulated (Barzilai et al., 2018). The goal of geroscience is to understand, target, and modulate fundamental biological aging processes to provide translational tools for fostering healthy aging in humans (Montano et al., 2022; Rolland et al., 2023).

In 2022, the Healthy Longevity Medicine Society introduced a new definition of what is now called healthy longevity medicine (HLM) as a discipline optimizing healthspan along the lifespan of an individual by targeting age-related processes. HLM embodies a multidisciplinary approach aimed at extending the healthy years of an individual’s life, preventing age-related diseases, and augmenting overall quality of life (QoL) through the amalgamation of diverse scientific and therapeutic strategies. The core principles of longevity medicine include the proactive detection and prevention of biological aging processes, personalized healthcare modalities tailored to individual needs, and the pursuit of an extended healthspan—the period of life lived in good health, free of frailty and disease (definitions proposed in Table 1).(Bischof et al., 2021; Rolland et al., 2023)

Table 1. Definitions of key terminology.

Adapted from HLMS https://hlms.co/ and Bischof et al., (2021))QoL: quality of life; FDA: Food and Drug Administration

The geroscience field is evolving at a tremendous pace. This is demonstrated within the preclinical realm, where geroprotective interventions, including personalized lifestyle modifications, supplements and repurposed drugs have been demonstrated to prolong lifespan and/or healthspan in animal models, leading to deceleration and possible reversion of age-related physiological decline (Gonzalez-Freire et al., 2020; Nielsen et al., 2022). Because repurposed drugs are already approved by the US Food and Drug Administration (FDA) with proven safety data and clinical protocols in place, there is considerable interest in testing their geroprotective potential in clinical trials aimed at demonstrating disease prevention and/or health optimization in human cohorts without frailty or disease (Table 1)(Kulkarni et al., 2022).

The Targeting Aging with Metformin (TAME) double-blind randomized-controlled trial (RCT) is one such trial designed to demonstrate the potential of repurposing metformin for longevity. The primary outcome of TAME is set to be the “time to event” of death and a cluster of age-related diseases, including cardiovascular events, cancer, cognitive decline, and dementia over a 6-year timeframe (Barzilai, 2017). However, this is an expensive and longer-term approach that could ultimately be circumvented by elucidating sensitive biomarkers that are predictive of age-related adverse outcomes and responsive to geroprotective interventions (Moqri et al., 2023). We propose that efforts to test and repurpose existing, safe geroprotective candidates should be immediately and greatly expanded beyond TAME, not only to have a more timely impact but also to increase the number of drugs potentially available to target aging in humans and mitigate the risks to the field, should any such trials fail to reach their desired outcome.

One way to do this effectively is by collecting “real-world evidence” (RWE) in which health data is collected from various sources within a heterogeneous population in a real-world setting (Table 1) (FDA, 2023). This trial design can be an effective tool to evaluate changes to healthspan metrics within a normative aging population as it is permissive to longitudinal data collection and can capture multiple data sources pertaining to health, daily life activities, and functionality (Chodankar, 2021; Dreyer, 2022; Rudrapatna and Butte, 2020). In particular, characterizing changes to biomarkers of aging is critical for demonstrating the effectiveness of geroprotective interventions on age-related outcomes within a realistic timeframe (Moqri et al., 2023).

The Biomarkers of Aging Consortium defines three main classes of aging biomarkers—molecular, physiological, and digital—to validate geroprotective interventions. Molecular biomarkers of aging, including -omics clocks, are proposed to be future gold standard endpoints for evaluating the efficacy of geroprotective interventions in normative aging cohorts; however, more data is needed to make sense of what aspects of aging these biomarkers are tracking and validate their influence on age-related outcomes (Moqri et al., 2023). In these early days of geroscience clinical trials, physiological biomarkers that evaluate clinical health metrics and functional performance remain the most reliable endpoints (May, 2023). We propose including QoL data within the physiological class of aging biomarkers because QoL assays are an important clinical tool for evaluating various aspects of functional health to distinguish healthy from unhealthy aging (Bottomley et al., 2019; Pais-Ribeiro, 2004). Further, QoL data facilitate the evaluation of key aspects of the real-world, functional consequences of geroprotective interventions that are not currently captured by the established classes of aging biomarkers, strengthening its case for inclusion.

In this review, we provide perspectives on the utility of QoL data as a candidate endpoint in geroscience clinical trials. In particular, we highlight the role of QoL assays to accelerate data collection in studies collecting RWE on repurposed drugs that aid in fostering healthy aging within normative aging cohorts. We underscore key metrics to consider when choosing appropriate QoL assays for such trials and emphasize the short-form 36 (SF-36) as the most well-suited candidate endpoint. Further, we provide evidence that QoL assays are currently underutilized as endpoints in geroscience clinical trials and propose that including QoL data, in conjunction with observed changes in the current biomarkers of aging, will provide additional information on healthspan-targeted interventions and their functional and real-world consequences.

2. Addressing the “Longevity Gap”: translating the promise of geroprotective interventions from animals to humans

The average global lifespan has more than doubled between 1800 and 2017 from 30 to 73 years(Roser, 2018) and children born today can expect to live 20 years longer than their grandparents born in the 1960 s(Hartenstein and Latkovic, 2022) Unfortunately, this increased life expectancy comes at a cost, as the share of our lives in poor health has not improved. For every extra year we have added to our lifespan, half of that may be in moderate or poor health (Coe et al., 2022). This is in large part the byproduct of a “healthcare” system that is overly focused on disease management at the expense of health optimization. Tackling one disease at a time leads to relatively smaller gains in healthy years as one disease follows another (Sierra et al., 2021). The expanding chasm between lifespan and healthy life is known as the Healthspan Gap, which is characterized by more time spent with poor QoL (Garmany et al., 2021). Based on the 2019 WHO health and life expectancy survey data, people spend an average of about 37% of their lives (20–30 years) in less-than-optimal health and 12% in poor health (Coe et al., 2022). These statistics suggest that the average individual spends more years in poor health and QoL today than ever before. This is reflected by an annual global healthcare spending of over $8 trillion USD, which is increasing faster than the gross domestic product (GDP) (Scott et al., 2021; Scott, 2023b; WHO, 2020a). The economic value of targeting aging was highlighted in a recent publication, which showed that delaying aging to increase healthy life expectancy by one year is worth $38 trillion USD in savings in the US alone (Scott et al., 2021).

The longevity field is redefining aging by distinguishing chronological age, how long we have been alive, from biological age, which reflects the health of all body organs, tissues, and cells as a function of time. Although chronological age is a leading risk factor for chronic disease, aging has classically been considered an inevitable process of degeneration and decay—a process that is stochastic and immutable. Therefore, clinical trials have primarily focused on validating drugs that address individual diseases rather than addressing the process of biological aging and the bouquet of pathologies emerging from it (Blagosklonny, 2023; Franceschi et al., 2018). A significant body of preclinical research demonstrates that the rate of biological aging can be modulated with geroprotective interventions that protect the body against the damages of the aging process by targeting fundamental molecular pathways that drive the biology of aging, known as the hallmarks of aging (HoA) (Table 1).(Keshavarz et al., 2023; Lopez-Otin et al., 2013, Lopez-Otin et al., 2023; Sun et al., 2022) There are currently 14 characterized HoA that describe the various damages and dysfunctions that accumulate within the body, eventually leading to frailty and disease.(Schmauck-Medina et al., 2022) This includes factors such as DNA damage, mitochondrial dysfunction, chronic inflammation, and loss of proteostasis, many of these being the focus for the discovery and development of molecular biomarkers of aging. Many of the HoA are strongly conserved between species, from single-celled organisms to mammals, catalyzing efforts for translating the promising preclinical data on geroprotective interventions from animal models to humans.(Lemoine, 2021; van der Rijt et al., 2020)

3. Metrics for validating candidate geroprotective interventions within geroscience clinical trials

The goal of geroscience clinical trials is to demonstrate the ability of geroprotective interventions to slow or reverse age-related phenotypes associated with biological aging, to prevent disease, optimize health, and ultimately improve QoL, thereby extending healthspan (Table 1).(Bischof et al., 2021) Once this is demonstrated, these interventions can be applied more rigorously on a personalized level in the clinic.

Observing significant differences in age-related clinical endpoints within a normative aging population is not feasible outside of large, longitudinal clinical trials. Running such lengthy trials to validate longevity interventions is expensive, resource-intensive, and an impediment to realizing the promise of longevity medicine within a tractable timeframe (Rolland et al., 2023). To validate geroprotective interventions in geroscience clinical trials, the longevity field would benefit from standardized health metrics that are quantifiable and sensitive enough to detect meaningful deviations from optimal health and QoL over time in individuals without frailty or disease.

Several ongoing efforts within the geroscience field are focused on elucidating sensitive and specific surrogate biomarkers of the aging process and validating their effectiveness for tracking age-related decline within tractable timeframes (Aging Biomarker et al., 2023; Moqri et al., 2023; Oh et al., 2023; Zhavoronkov et al., 2019). Measuring biological age requires sophisticated tools that evaluate molecular, cellular, and functional health changes associated with older chronological age and, more importantly, serve as metrics to predict multi-morbidity and adverse health outcomes.(Wu et al., 2021)

Aging biomarkers have been grouped into digital, physiological, and molecular classes by the Biomarkers of Aging Consortium. The digital biomarkers class includes measures typically obtained by wearable technologies such as tracking heart rate variability from smart watches and sleep quality from sleep monitoring rings. Physiological biomarkers are clinical measures of functional performance and physical characteristics that can include variables such as VO2 max, gait speed, cognitive assessments, and visceral adiposity from DEXA scans. Molecular biomarkers include single molecules (e.g. interleukin (IL)-6, NAD+) and omics measurements (e.g. epigenomic, proteomic, metabolomic) and can be reflective of changes to the HoA (Moqri et al., 2023). Within the class of molecular biomarkers, omics measurements have been utilized to generate biological age clocks, which have been increasingly used as candidate biomarkers of aging in geroscience clinical trials and are proposed to be future gold standard endpoints for evaluating the efficacy of geroprotective interventions in normative aging cohorts (Moqri et al., 2023; Zhavoronkov et al., 2021). In principle, if a test accurately measures biological age, it also estimates remaining healthspan, since biological age is the greatest risk factor for most functional declines and diseases.(Kaeberlein et al., 2015; Liu et al., 2023)

Efforts are being made in current geroscience clinical trials to measure molecular biomarkers and relate them to age-related health outcomes in humans; however, more data is needed to make sense of what aspects of aging these biomarkers are tracking, as well as their influence on age-related outcomes (Cummings and Kritchevsky, 2022; Galkin et al., 2020; Jansen et al., 2021; Palmer, 2022; Sandalova et al., 2023; Zhang, 2023). Collecting molecular biomarker data alongside physiological biomarkers is of particular importance as the latter is directly linked to clinical and functional declines in health and already accepted as endpoints for disease-focused clinical trials. While the currently defined class of physiological biomarkers include body type, physical performance, and cognitive performance, we emphasize including QoL data in this class because QoL assays are established clinical tools for evaluating various aspects of functional health and wellness that are integral to healthy aging but not currently captured by the established classes of biomarkers of aging (Coe et al., 2022; Moqri et al., 2023). This includes data across multiple dimensions, including physical, mental, and social domains of life (Kaplan and Hays, 2022; Williams and Wood-Dauphinee, 1989). Further, QoL assays have shown to be instrumental in quantifying and elucidating the nuances of functional decline within normative aging populations (Krawczyk-Suszek and Kleinrok, 2022).

The WHO defines healthy aging as “the process of aging while developing and maintaining the functional ability that enables well-being in older age”.(Wang et al., 2023) This suggests that older individuals can achieve healthy aging by enhancing their physical, social, and mental health to the maximum extent possible to facilitate their active roles in society (Wang et al., 2023). While molecular biomarkers provide mechanistic information characterizing how a healthspan-enhancing intervention is influencing the biology of aging, QoL assays provide descriptive insights pertinent to understanding how these changes affect an individual’s day-to-day experience and functioning, an important and often neglected component of healthspan (Baumhauer, 2017; Bottomley et al., 2019; Pais-Ribeiro, 2004). Additionally, QoL assays that evaluate multiple disparate aspects of health are particularly suited as endpoints within geroscience clinical trials because candidate geroprotective interventions, by their very nature, should prevent the emergence of multiple aspects of age-related physiological decline.

4. Accelerating validation of geroprotective interventions through collecting RWE on repurposed drugs

Efforts to translate insights from animal studies to human clinical trials are advancing with the goal of proving the safety and efficacy of geroprotective interventions that protect the body against the damages of the aging process, thereby extending healthspan. There are several promising longevity interventions, including, but not limited to, lifestyle interventions, supplements, drugs, gene therapy, and cellular reprogramming. Those interventions that may have the greatest impact on healthy longevity medicine in the immediate future are repurposed drugs (e.g. metformin, rapamycin, and canagliflozin), personalized lifestyle interventions (e.g. exercise, diet, and sleep optimization), and supplements (e.g. NAD+ boosters, alpha-ketoglutarate, and urolithin-A). In particular, FDA approved drugs with established human safety and efficacy data in multiple disease demographics, and demonstrated efficacy for enhancing lifespan and healthspan in preclinical studies are prime candidates to be repurposed in geroscience clinical trials (Table 1) (Kulkarni et al., 2022; Rolland et al., 2023).

The TAME trial was designed, in consultation with the FDA, as a robust proof-of-concept study to demonstrate the geroprotective benefits of metformin by evaluating “time to incidence” of a cluster of chronic diseases in elderly individuals.(Barzilai, 2017) The TAME trial is planned to provide a template for future studies validating FDA approved drugs with potential to be repurposed as geroprotective interventions; however, it is expensive, lengthy, and the outcome carries an outsized impact on prospects for the longevity field (Kulkarni et al., 2022; Rae, 2023). We propose the immediate expansion in efforts to assess and repurpose existing, safe geroprotective candidates beyond TAME. This would result in faster impact on the field, increase the number of potentially-available interventions to target human aging, and mitigate some of the risks to the field, should trials fail to reach their desired outcome.

One way to do this effectively is by collecting RWE in which an array of health and QoL data is collected from individuals as unobtrusively as possible, outside of a formal clinical setting. This trial design can be an effective tool for validating the safety and real-world functional consequences of geroprotective candidates, as it is permissive to accessing large datasets that reflect changes to health status, functional capacity, and QoL more rapidly and less expensively than brick and mortar trials. RWE trials can be prospective observational studies or RCTs, and may collect data from electronic medical records, medical claims, product or disease registries, and other sources (e.g. digital health technologies, health questionnaires, and adverse event reports).(FDA, 2023) The accessibility and convenience of such studies allows for tracking of a larger, more heterogeneous subject cohort across a diverse geographic and demographic range, and over extended periods of time.(Chodankar, 2021; Dreyer, 2022; Rudrapatna and Butte, 2020)

The advent of digital health has improved the accuracy and applicability of digital wearables and made classically inaccessible diagnostic tools accessible, which has strengthened the rigor of such studies.(Baumhauer, 2017; Kaplan and Hays, 2022; Touray, 2018) The future of RWE geroscience clinical trials will integrate several different health data points to inform the efficacy of geroprotective interventions on healthspan optimization (Fig. 1).

5. The role of QoL measures for collecting RWE in geroscience trials

5.1. QoL assays for characterizing normative aging

A few large longitudinal studies, involving thousands of individuals followed over several years to decades, have utilized QoL data as ancillary data points to support the characterization of the functional decline that accompanies the aging process in normative and healthy aging cohorts. These include the Baltimore Longitudinal Study of Aging, The Chinese Longitudinal Healthy Longevity Survey, The New England Longevity study, and the LonGenity centenarian study (Jopp et al., 2016; Kuo et al., 2022; Newman and Murabito, 2013; Poon et al., 2010; Yi, 2008). These studies on longevity and healthy aging most commonly use the short form 36 (SF-36) QoL assay which consists of 36 questions evaluating 8 distinct domains of health and wellness (Attafuah et al., 2021; Junaković et al., 2023; Selim et al., 2005; Tate et al., 2023; Yates et al., 2008).

One such study conducted by Krawczyk-Suszek et al. utilized the SF-36 to characterize normative aging over a 2-year time frame in more than 1000 healthy individuals over the age of 65 (Krawczyk-Suszek and Kleinrok, 2022). This study proposed that the SF-36 could serve as a tool to capture pre-disease states of sub-optimal health influenced by smoking status and other factors that impact biological age, such as employment status, location, and physical activity, among other lifestyle factors. Obtained data show that the QoL and functional capacity of the study group deteriorated over the two-year timeframe, despite the absence of chronic disease.(Krawczyk-Suszek and Kleinrok, 2022) This type of study illustrates the sensitivity of QoL measures as a metric for age-related health changes, even in healthy cohorts.

Another interesting demonstration of the utility of QoL assays in geroscience clinical trials is a multi-center cross-sectional study in which health-related QoL and a frailty index were utilized as metrics to evaluate healthspan in 586 companion dogs (Chen et al., 2023; McKenzie, 2023; Rolland et al., 2023) Even though this was not a human study, companion dogs are considered a robust model for human aging given their similar genetic heterogeneity, shared environmental exposures, and lifestyle habits (physical activity, diet) with humans (Creevy et al., 2022). This study showed that QoL was stable in young dogs but began showing yearly declines across most health domains (energy, happiness, comfort, and calmness) once the dog reached 7 years of age, which represents approximately mid-life for dogs (Rolland et al., 2023). Further, larger older dogs declined faster than smaller older dogs, consistent with the knowledge of canine biological aging (Kraus et al., 2013). Another important finding of the Canine Healthspan study was that the frailty index and QoL outcomes both identified the same age-related changes even though the frailty index was a medical assessment by a veterinarian and QoL was a questionnaire evaluating the owners’ perceptions of their dogs at home. This consistency is strong evidence for the validity of QoL assays in tracking age-related decline associated with accelerated biological aging (Chen et al., 2023; McKenzie, 2023).

5.2. QoL assays for chronic diseases of aging

With guidance from the FDA, patient-reported outcomes are increasingly being used within disease-focused clinical trials (Haslam et al., 2020). Many well-validated QoL assays have been developed for clinical trial endpoints assessing diseases likecancer, Alzheimer’s disease (AD), diabetes, cardiovascular disease, mental health disorders, and various syndromes (e.g. chronic fatigue and irritable bowel). In most clinical trials QoL assays are utilized as secondary endpoints supporting a primary biological endpoint that reflects disease progression (Megari, 2013). Some trials have applied QoL assays in individuals with heart failure, which revealed that lower QoL scores following cardiac procedures are predictive of increased incidence of morbidity and mortality (Megari, 2013). In oncology trials QoL data are used extensively as drugs are costly and may only result in modest gains in overall survival. Improved QoL during that time is an important consideration for evaluating a patient's functioning and wellbeing and therefore cost/effectiveness analysis.(Haslam et al., 2020) Further, the use of QoL assays opens new avenues for enhancing outcomes among cancer patients and survivors, giving new dimensions and tools for precision triage and predictions, and are a crucial aspect of post-treatment care. One example is the integration of QoL assessments to create functional profiles that monitor and enhance therapeutic decision making in this patient population (Anwar et al., 2014; Buiting and Olthuis, 2020; Sim et al., 2020). Increasingly, to enhance the quality of healthcare QoL data are leveraged to inform personalized health interventions, aiming at the proactive management of health and disease prevention, offering a promising avenue for improving the aging process for a diverse patient demographic (Tsang and Loh, 2020).

For longitudinal studies, administering QoL assays at multiple timepoints throughout the length of the trial can help determine both direction and magnitude of health changes (Jacobsen et al., 2002). Within geroscience clinical trials, collecting QoL data throughout the trial and at follow-up (post-intervention) can help determine if a participant is consistently benefitting and whether discontinuing treatment results in changes in the rate of age-related decline.

The reliability of these assays as endpoints within geroscience clinical trials largely depends on the types of questions selected and the training dataset utilized to develop the scoring system. While many QoL assays are designed to be sensitive to clinically important changes that are reflective of the progression of a specific disease indication, some are more generic, with questions and scoring systems designed to assess a broad range of health domains and detect age-related functional changes that are relevant to the progression of comorbid conditions or even changes within a normative aging population (Baumhauer, 2017; Bourdel et al., 2019; Megari, 2013; Siette et al., 2021). The latter tools are particularly interesting for use as endpoints within geroscience clinical trials.

The frequency of administration of QoL assays in clinical trials depends on the research questions and the length of the study. QoL data are typically collected at baseline (before intervention), followed with one or more assessments during the intervention trial, and often during post-intervention follow-up (Jacobsen et al., 2002). Administration every 3–4 months is common in clinical trials evaluating the efficacy of interventions addressing specific age-related diseases, such as type 2 diabetes and cardiovascular disease (Bourdel et al., 2019; Luscombe, 2000; Soriano et al., 2010; Vickers, 2006). Due to their feasibility of administration and low cost, QoL assays can be administered frequently and longitudinally within geroscience clinical trials characterizing how a geroprotective intervention influences the progression of the aging process. Within observational studies and registries, they can even be administered over the span of years, and may be used to follow up on the QoL of an individual post-intervention (as is often done in oncology trials) (Haslam et al., 2020).

5.3. Current and past use of QoL assays in geroscience clinical trials

To establish whether QoL assays are being used in previous, current, and planned geroscience clinical trials, we performed a search on clinicaltrials.gov. We assessed the use of the SF-36, EQ-5D, and WHO-BREF as outcome measures in trials; we excluded the 15-D from this search due to inaccuracy in retrieved records with this search term and the AQoL-8 for lack of records using this outcome measure. The search was performed in January 2024, and search terms for condition/disease were aging OR longevity OR healthspan. The results were filtered for adults (age: adult (18−64) and older adult (65+)), and we filtered out any studies with the status terminated, suspended, and withdrawn. We also filtered for trials open to healthy volunteers (Filter: accepts healthy volunteers). This search resulted in 1997 records of total geroscience interventional trials. To establish how many of these used the SF-36 as an outcome measure, we extended the search to include the search terms SF-36 OR SF36 OR short-form 36 for outcome measures within this dataset. This revealed 94 studies using the SF-36 as an outcome measure, which reflects 4.7% of the total number of geroscience interventional studies in adults. The outcome measure EQ-5D OR EuroQoL-5D revealed 29 studies, reflecting 1.5% of geroscience interventional trials using the EQ-5D. These data confirm the limited use of QoL assays in geroscience interventional trials. These data are limited to only reflecting trials registered at clinicicaltrials.gov, and have limitations regarding the use of the search terms in the records.

5.4. QoL assays for geroscience clinical trials

The Participatory Evaluation of Aging with Rapamycin for Longevity (PEARL) trial (clinicaltrials.gov: NCT04488601) is one example of a large, decentralized effort collecting RWE to characterize the effects of a promising repurposed drug on the aging process within a normative aging human population. The PEARL trial is a 12-month double-blinded, randomized, placebo-controlled study that was completed at the end of 2023, and the data resulting from this trial are expected to help validate the efficacy of rapamycin on reducing visceral adiposity, plasma biomarkers of metabolic health, several different biological age diagnostic tests such as epigenetic clocks, and effects on real-world functional health and QoL changes using a well-validated QoL assay (SF-36) (Konopka et al., 2023; Lu et al., 2019; Rondon Garcia and Ramirez Navarrro, 2018).

We propose that including QoL measures as endpoints in geroscience clinical trials can help validate healthspan-targeting interventions by demonstrating improvements in aspects of healthspan that are difficult to measure with biological markers, such as pain levels, fatigue, functional role limitations, factors affecting daily activities, and subjective measures of health, like emotional wellness and social functioning. Further, questionnaires can be administered frequently electronically and tracked alongside other health changes, exposures, and life events to more completely characterize changes in healthspan across longitudinal trial timeframes. More recent technological developments enable questionnaire links to be shared widely via tools such as smartphone apps, facilitating quick and large data collection efforts (Schatz, 2015). Collecting information on recent life events, such as changes in occupation, location, and loss of loved ones, will add further context to observed changes in QoL scores and biomarkers, since these events are known to temporarily affect metrics(Gerth van Wijk, 2005; Lu et al., 2019; Rondon Garcia and Ramirez Navarrro, 2018).

One common reservation about QoL assays is their accuracy in evaluating the efficacy of interventions compared to more objective clinical measures (Haraldstad et al., 2019; Lin et al., 2013). Studies suggest that, in some cases, QoL assays such as the SF-36 and the Patient-Reported Outcomes Measurement Information System (PROMIS) can rival gold-standard robust clinical technologies in the accuracy of evaluating objective health metrics like physical function (Lahoud et al., 2017). For example, one 52-week study compared physical function scores obtained using PROMIS with the GAITRite temporal and spatial gait-analysis system in 106 patients who underwent knee ligament construction (Baumhauer, 2017). The study found that functional outcome data from both assays were significantly correlated, but the PROMIS was not limited by a ceiling effect and could detect significant improvements in function 32 weeks beyond that of the GAITRite. The GAITRite system directly measures walking speed, cadence, stride length, and other gait parameters–costing $52,000 USD. The PROMIS is a QoL assay that includes a physical function questionnaire that is less than a fraction of the cost and takes one minute to complete (Baumhauer, 2017).

Another common reservation about self-reported questionnaires is their vulnerability to the placebo effect. Although research has suggested that the magnitude of placebo effect reported in studies is heavily influenced by how trials are conducted and patients informed, placebo responses are especially problematic in studies that rely on subjective patient-reported outcomes and can have detrimental effects on assay sensitivity. Studies suggest that within longitudinally designed trials, running 6 months to 1 year, the placebo effect can be expected to diminish over time (Hansen et al., 1996; Hróbjartsson and Gøtzsche, 2010). Further, methodologies can be leveraged to buffer the placebo effects, which involve integrating a pre-training period within the study design in which subjects do not receive any treatments and practice accurate self-reporting(Evans et al., 2021), including functional assessments that are less vulnerable to placebo, and neutralizing subject expectations before the study starts (Evans et al., 2021; Hodgins et al., 2018). Additionally, statistical methods can be utilized when analyzing data to help address the placebo effect, but these are outside the scope of discussion for this perspective (Kessels et al., 2019; Raman, 2020).

Intriguingly, recent evidence suggests that psychosocial questionnaire-based evaluation of an individual’s perception of their health status and age (i.e. subjective age) more accurately predicts the risk of disease than an individual’s chronological age, and subjective age is modifiable based on lifestyle interventions. AI-generated “subjective age clocks'' generated from large datasets reveal that self-reported questionnaires that evaluate an individual’s perception of their health may provide important insights for validating the efficacy of gerotherapeutic interventions on age-related outcomes.(Zhavoronkov et al., 2020) Studies such as these suggest that self-reported questionnaires that have been well-validated to characterize age-related outcomes may provide valuable information about the effects of healthspan-enhancing interventions in future trials.

6. Utilizing QoL data to operationalize intrinsic capacity and evaluate healthspan within geroscience clinical trials

The Mckinsey Health Institute has defined the four dimensions of health that make up healthspan as physical, mental, social, and spiritual (Hartenstein and Latkovic, 2022). The WHO has proposed a framework to measure various aspects of health that encompass healthspan called intrinsic capacity (IC). IC is a composite measure of an individual’s cognitive function, sensory function, locomotor function, psychological well-being, and vitality (WHO, 2020b). In March 2022, the Geroscience Task Force proposed operationalizing the IC framework as a composite primary endpoint in geroscience clinical trials (Rolland et al., 2023). The task force cited the Clinical Dementia Rating sum of boxes (CDR) assay, recently recommended by the FDA as an endpoint for AD prevention trials, as a precedent for their proposal (Rolland et al., 2023). The CDR assay is a composite cognitive score that collects data (via interview format) on various aspects of a patient’s life, including personal care, community affairs, home and hobbies, judgment, and problem-solving (Rolland et al., 2023). QoL assays have been shown to be sensitive to changes over time and have demonstrated both predictive and prognostic utility for assessing functional ability in various disease outcomes (Krawczyk-Suszek and Kleinrok, 2022; Siette et al., 2021). Well-validated QoL assays could be used within geroscience clinical trials (along with other clinical assessments) to monitor IC trajectories over time and predict disability and disease and, therefore, projected healthspan, similarly to the CDR sum of boxes score for AD.

7. Candidate QoL endpoints for geroscience clinical trials

7.1. Criteria for evaluating the validity of QoL assays for geroscience clinical trials

One of the main barriers preventing the integration of health-related QoL endpoints in research is a lack of consistency in the types of QoL assays selected within a specific health discipline as well as how they are analyzed and interpreted, making comparing results across different studies challenging (Bottomley et al., 2018). The geroscience field, which is still in its early stages, has a unique opportunity to integrate QoL questionnaires as endpoints to facilitate data collection and interpretation standardization moving forward (Tsang and Loh, 2020). Validated QoL questionnaires are suitable for longevity RCTs, observational trials, as well as n=1 trials.

We propose eight criteria for evaluating the validity of a QoL questionnaire, adapted to inform use as endpoints in geroscience clinical trials (Bourdel et al., 2019; Siette et al., 2021). A good geroscience endpoint can detect intervention-specific changes in a meaningful time period (responsiveness), measure changes even when these are small in a normative aging population (sensitivity), and provide reproducible results when used by various groups (reproducibility). Furthermore, for the evaluation of aging outcomes, an assay should have a scoring system that is validated in a large and relevant normative aging population (data quality), and the test should be generic, allowing for comparisons between people with various health backgrounds and conditions and/or the healthy population (disease non-specificity). Finally, the assay should have a manageable number of questions that can be answered in a limited time as well as being cost permissive and accessible (feasibility), the questions should pertain to age-related decline and dysfunction, avoiding the floor/ceiling effect (question quality), and reflect multiple different dimensions of health and QoL (multidimensionality) (Table 2) (Bourdel et al., 2019; Siette et al., 2021). Two criteria that require rigorous vetting before QoL assays can be used in geroscience trials are sensitivity and data quality.

Table 2. Evaluation of the utility of QoL assays for geroscience clinical trials.

Criteria for validity of QoL assays for geroscience trialsFeaturesSF-36EQ-5DWHOQOL

✅Meets criteria, ❌Does not meet criteria. 15D: 15-dimensional; AQOL-8: assessment of quality of life-8; EQ-5D: European Quality of Life 5 Dimension; SF-36: short-form 36; QoL: quality of life; WHOQOL-BREF: World Health. Organization Quality of Life - BREF; Table adapted from Siette et al., (2021) and Bourdel et al., (2019)

The data quality of QoL assays is largely determined by the dataset utilized to generate a scoring system. The scoring systems for QoL assays (typically Likert scales) are developed based on answers from subject cohorts within large, epidemiological public health studies. This must be considered when evaluating their effectiveness, similar to training datasets for biological age clocks (Bernabeu et al., 2023; Siette et al., 2021; Wang et al., 2023). Some examples of such studies are the Longitudinal Study of Aging (LSOA), the Health and Retirement Study (HRS), the National Health and Nutrition Examination Survey (NHANES), the Medical Outcomes study, the National Health Interview Survey (NHIS), and the Medical Expenditure Panel Survey (MEPS) (CDC, 2015; Kaplan and Hays, 2022; Pequeno et al., 2020; Sonnega et al., 2014; Stanziano et al., 2010; Weng et al., 2020; Yin et al., 2016). Those surveys trained in larger, more diverse cohorts within the context of a normative aging cohort are more generalizable and applicable for use in geroscience clinical trials than those trained in disease-specific, less diverse cohorts (Table 2) (Kaplan and Hays, 2022).

The sensitivity of QoL assays is another factor that must be validated for geroscience clinical trials as they involve a normative aging population rather than individuals with disease. The sensitivity of QoL assays can be tested by evaluating changes in QoL scores within large, longitudinal datasets (e.g. UK biobank) and how they predict age-related outcomes, morbidity, and mortality. The rapid evolution of the field of biological age diagnostics demonstrates how advancements in AI-based big data analytics have increased the impact of data from existing longitudinal datasets (Zhao et al., 2019; Zhavoronkov et al., 2021; Zhavoronkov et al., 2019). For QoL assays to be effectively utilized, the geroscience field needs to accept and standardize a core set of QoL measures reflecting multiple health dimensions and with determined validity, sensitivity, and reliability. Future geroscience clinical trials will have to further validate the sensitivity of QoL assays by observing how changes in QoL scores correlate with known changes in biomarkers of aging and health outcomes in response to geroprotective interventions.

7.2. Candidate QoL assays

Based on the evidence reviewed and the above criteria, we have identified a few QoL assays that stand out as particularly relevant for geroscience clinical trials application based on widespread utilization as a clinical tool (validated in clinical trials), broad utility and data quality (tested in diverse cohorts, including normative aging), sensitivity, and responsiveness within a normative aging cohort. These include the WHO Quality of Life-BREF (WHOQOL-BREF), European Quality of Life 5 Dimension (EQ-5D), Assessment of Quality of Life (AQoL-8), 15-dimensional (15D), and the SF-36.(Bourdel et al., 2019; Kaplan and Hays, 2022; Krawczyk-Suszek and Kleinrok, 2022; Siette et al., 2021) Refer to Table 2 for a summary of the strengths and weaknesses of these different tools and their potential application within geroscience clinical trials based on the eight defined criteria. For a more comprehensive evaluation of the history, strengths, challenges, and applications of available QoL assays for research and clinical trials in general, refer to Siete et al.(Siette et al., 2021) and Bourdel et al.(Bourdel et al., 2019)

Based on the evidence reviewed we found that some studies challenged the question quality and sensitivity of the EQ-5D as it consists of only 5 questions and is vulnerable to the ceiling effect (Table 2). The question quality of the 15D was similarly challenged leading to an observed ceiling effect in certain cohorts (Table 2). Further, there is some conflicting evidence on the responsiveness of the WHOQOL-BREF and AQoL-8 (Table 2). The SF-36 was found to be the most widely utilized and well validated generic QoL assay to date and is particularly useful for evaluating health decline in a normative aging population, as the scoring system for the questionnaire was developed based on the US general population within the Medical Outcomes Study (Table 2) (Abbasi-Ghahramanloo et al., 2020; Bourdel et al., 2019; Kazis et al., 2004; Krawczyk-Suszek and Kleinrok, 2022; Siette et al., 2021).

In a systematic review including 293 studies evaluating 29 QoL questionnaires, Siette et al. found that the AQoL-8, EQ-5D, and SF-36 were the most utilized QoL assays within primary care; the SF-36 was the most commonly utilized assay in aged care settings, and the EQ5D and SF-36 were the most commonly used questionnaires across various settings (including clinics, assisted living facilities, and residential aged care facilities).(Siette et al., 2021) Additionally, the SF-36 covered the largest range of health domains besides the WHOQOL-BREF.(Siette et al., 2021) Although superiority studies directly comparing the different QoL assays and their utility as clinical trial endpoints (especially within normative aging cohorts) are lacking, a few landmark studies comparing QoL assays have demonstrated the SF-36 as the best validated, across different diseases, regarding responsiveness, sensitivity, reproducibility, and question quality.(Kaplan and Hays, 2022; Siette et al., 2021) Based on results from our literature review on the utilization of QoL assays within geroscience clinical trials and the evidence evaluating alignment with the eight criteria defined above (Table 2), we propose the SF-36 as the most suitable QoL assay to be repurposed as an endpoint within geroscience clinical trials.

7.3. The unique value of the SF-36 for evaluating healthspan metrics

The SF-36 is a 36-item survey developed based on the Medical Outcomes Study and has been used for over three decades in clinical trial settings. The SF-36 evaluates multiple domains of QoL that provide a holistic picture of functional health and QoL. Individuals receive a score for each health domain that ranges from 0 to 100, with scores in the ranges 50–60 representing the “average score” for middle-aged adults in the US.(Ware, 1993; Ware and Sherbourne, 1992)

The eight health domains of the SF-36 include physical functioning, pain, energy and fatigue, emotional wellness, general health, social functioning, and role limitations due to physical and emotional health.(Jenkinson et al., 1993) While it is intuitive how some of these factors are integral components of healthspan (e.g. physical functioning, role limitations, and pain), research is revealing just how vital the other domains are. For example, recent research suggests that social isolation affects morbidity and mortality similarly to smoking 15 cigarettes daily.(Kidambi and Lee, 2020) Emotional wellness exists in a spectrum, and studies have shown that severe emotional health disorders can reduce lifespan by 10–25 years.(Pan et al., 2020) Studies have shown that chronic fatigue is correlated with the HoA, including poor mitochondrial health, metabolic dysfunction, and underlying inflammation.(Bland, 2017; Huth et al., 2020; Komaroff, 2017; Lakhan and Kirchgessner, 2010) This is especially relevant as low energy and fatigue affect over 1 in 5 adults in the US.(Yoon et al., 2023) Healthy aging and healthspan, by its very definition, is contingent upon the ability to maintain function throughout the aging process.(Wang et al., 2023) Compromised functional ability and role limitations are some of the most consequential byproducts of advanced biological age, alongside morbidity and mortality, and can serve as informative endpoints for assessing the real-world efficacy of healthspan-enhancing drugs within a normative aging cohort. When an individual’s functional capacity and typical roles become compromised, this is often accompanied by a diminishing sense of purpose, especially if a strong social support network is absent.(Colon-Emeric et al., 2013; Escobar-Bravo et al., 2012; Muhandiramge et al., 2022) A recent survey by the McKinsey Health Institute suggests that “purpose in life” is the top factor influencing individual health outcomes.(Ahlawat et al., 2023)

SF-36 scores have been shown to be effective in predicting outcomes of several different diseases and all-cause mortality. This includes heart disease, AD, metabolic disease, renal disease, cancer, and mental health outcomes.(Bjorner et al., 2013; Ding et al., 2020; Ezzati et al., 2019; Nilsson et al., 2020; Tapak et al., 2022) We propose that the SF-36 can also be useful for characterizing healthspan trajectories, specifically the transition from states of optimal health to sub-optimal health to poor health. Moreover, the SF-36 may serve as an accessible and reliable tool to evaluate the effects of healthspan-enhancing interventions in geroscience clinical trials, in which the goal is to raise health, lengthen life, and compress morbidity (Fig. 2). Thus, in our perspective, the SF-36 is a viable endpoint for generating RWE on the effects of geroprotective interventions on various facets of age-related functional decline that are often neglected within geroscience clinical trials.(Hayes et al., 1995; Jenkinson et al., 1993; Obidoa et al., 2010)

7.4. The risks of not including QoL measures as endpoints within geroscience clinical trials

If QoL measures are not integrated into geroscience clinical trials, we risk neglecting critical components of healthy aging and healthspan that encompass “real world” functional ability and the human experience. Factors that provide individuals with autonomy and agency in life, such as mental health, social functioning, and limitations in daily activities, play a critical role in both the biological and psychological process of healthy aging.(Kim et al., 2021; Saadeh et al., 2020)

The elucidation of reliable molecular biomarkers of aging will likely be the most important advancement for the longevity field, but a possible consequence of having a myopic focus on these markers is neglecting to consider the “real world” impact geroprotective and personalized lifestyle interventions have on patients’ lives. What good is a 5-year reversal in epigenetic age if that is not linked to a meaningful, real-world outcome? We propose including QoL data as an additional biomarker of aging within the physiological class of biomarkers, as QoL questionnaires play an important role in making sense of the “real world”, functional implications of changes in biological markers of aging that define the aging process.(Moqri et al., 2023)

The geroscience field is revealing several interventions that are thought to have overlapping roles in mitigating the risks of developing age-related disease and functional decline.(Gonzalez-Freire et al., 2020; Guo et al., 2022) QoL questionnaires allow for the tracking of changes to multiple distinct domains of functional health and QoL in response to longevity interventions using a single assay. In doing so, they may help distinguish the differential effects of healthspan-enhancing interventions for improving distinct functional aspects of age-related decline and healthspan.(Krawczyk-Suszek and Kleinrok, 2022)

Beyond its utility on the individual level, QoL scores have originally proven to be useful on the population level, where it has been used in health economics(McGuire, 2017) to inform how well different medical treatments improve patients’ lives, serving as a fundamental component of cost-effectiveness analysis (calculating metrics like quality-adjusted life years (QALY)) in the US and worldwide.(Prieto and Sacristan, 2003; Sanghera and Coast, 2020) This is another argument for integrating QoL into biological age determinants since the longevity dividend is largely dependent on the QALYs.(Scott, 2023a) Furthermore, QoL data, benchmarked against normative age group data, provide valuable insights for health policy and care standards, underscoring their significance in the holistic assessment of aging processes.(Kaplan and Hays, 2022)

The field of healthy longevity medicine is focused on the prevention of disease and frailty, but importantly, also the optimization of health in every stage of life.(Bischof et al., 2021) QoL questionnaires offer the opportunity for frequent and rapid data collection of the real-world impacts of longevity interventions in enhancing the lives of its users along their lifespan.

8. Conclusions

Empowered by promising research suggesting that the aging process can be slowed down, there is an emerging shift in how we view health on a public health level. Health status was (and in many ways, still is) characterized by the simple presence or absence of disease, with clinicians reactively applying interventions as disease arises. Health is now more broadly being recognized as a continuum characterized by the consequences of biological aging that consist of a broader composition of physical, mental, social, physiological, and functional capacity–even integrating components of well-being and purpose–irrespective of the presence of disease.(Zhao et al., 2021; Zhou et al., 2023)

While geroprotective interventions have been well-validated in animal models to improve lifespan and healthspan, designing and implementing geroscience clinical trials in humans is still a developing domain.(Rolland et al., 2023) Several rate-limiting factors challenge the field's ability to collect a sufficient amount of data that rigorously demonstrates the effectiveness of geroprotective interventions within normative aging cohorts. One of the major challenges is choosing the appropriate biomarkers and endpoint(s) that are predictive of adverse age-related outcomes, clinically acceptable, easy to collect, meaningful within the context of an individual’s life, and sensitive to change in a healthy population within a tractable timeframe. To address these challenges and accelerate data collection within the field, we highlight the value of utilizing QoL data as an endpoint within geroscience clinical trials that collect RWE on repurposed, geroprotective drugs. Further, we propose its inclusion in the current list of candidate biomarkers of aging as it facilitates the evaluation of key aspects of the real-world; functional consequences of geroprotective interventions that are not currently captured by the established classes of aging biomarkers.

In this review, we proposed a framework for the application of QoL assays in collecting RWE on the efficacy of repurposed, geroprotective drugs. We provided key metrics to consider when evaluating and selecting appropriate QoL assays and highlighted the evidence supporting the SF-36 as an ideal candidate QoL endpoint. Designing trials that utilize QoL assays alongside other technologies that collect real-world data (e.g. digital wearables, web-based cognitive tests, electronic health records, and at-home blood tests) may prove to have a more timely impact on the field and increase the number of drugs potentially available to target aging in humans (Fig. 1). Ideally, such trials will include a biobank that facilitates post-hoc measurement and analysis of an expanded array of aging biomarkers and biological aging clocks to help the field better understand their role as geroscience endpoints. Overall, this framework may play a critical role on the path to validating healthspan-enhancing drugs and their application in healthy longevity medicine.

Declaration of Competing Interest

none.

Acknowledgements

The authors would like to thank Francesca Compau for helpful discussions and generation of images.

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